searching for superstars isn’t the answer how ...€¦ · too many organizations are focusing...

Searching for superstars isn’t the answer How organizations can build world-class analytics teams that deliver results

Great things in business are never done by one person. They’re done by a team of people

Steve Jobs

Searching for superstars isn’t the answer How organizations can build world-class analytics teams that deliver results

Organizations across Canada are racing to exploit the power of analytics to transform themselves into truly insight-driven organizations (IDOs). They’re eager to use vast amounts of data to deliver superior customer experiences, unlock new competitive advantages, and open up new opportunities for profitable growth.

This focus on analytics has made finding and engaging analytics talent a critical business issue for organizations in every industry. According to Deloitte’s 2017 Global IDO Survey, 70 percent of respondents said they planned to hire analytics talent in the next six months. In Canada, the need is certainly strong: 60 percent of organizations currently lack a dedicated analytics team. Thankfully, they have a rich talent pool to draw from: Canada is increasingly renowned for the world-class quality and skills of its data analysts, engineers, modellers, and others with the knowledge and experience needed to help businesses transform through analytics and artificial intelligence.

However, many organizations have discovered that recruiting talent isn’t having the desired impact. Only a quarter of executives surveyed for Deloitte’s Industry 4.0 Survey study are highly confident that they have the right workforce compositions and the skillsets needed for the future. Executives complain about misalignments and disconnects between what analytics delivers and what the business needs, and grumble about analytics teams spending time “boiling the ocean.” Fifty-eight percent

of respondents to our Global IDO Survey report they face challenges in trying to act on the insights derived from enterprise data. Seventy-five percent report their analytics functions remain siloed and local in outlook. Two-thirds wish their analytics teams had more sophisticated skills in such areas as visualization and user experience.

Why are organizations’ analytics recruitment efforts failing to deliver the anticipated results? It’s because they’re creating unbalanced teams that simply aren’t set up to provide what the business needs now, leading to frustration and dissatisfaction on all sides.

Too many organizations are focusing their analytics recruitment on highly rare superstars with stellar skills. Often, given many organizations don’t know what successful analytics teams look like, this ends up being a PhD with superb technical or quantitative skills and a vague “data scientist” job title. In other cases, organizations search high and low for the elusive, highly experienced data engineers, statisticians, quants, and others who are experts not only in finding and modelling the right data, but who can turn it into the kind of powerful insights that lead to fast action and rapid results.

But it doesn’t work that way.

For one thing, such superstars are rare, costly—and often difficult to retain, creating new risks for the business. Building an entire team of outstanding talent would be ruinously expensive for most organizations, even if they could manage to attract so many in the first place.

In many ways, it’s like a hockey team that needs to operate within the confines of salary caps, wage structures, contractual obligations, and other factors that limit what management can spend on talent. Heavy investment in one or two individuals can leave a team shorthanded in other equally important areas. And we’ve all seen cases where teams that rely on one or two players—or even teams filled with expensively assembled talent—have failed to deliver the expected results. Analytics teams are no different, because often superstars bring only part of what a truly effective team needs to thrive. To put it another way, a champion team will always be better than a team of champions.

Effective insight-driven organizations know that analytics teams need to comprise a range of capabilities, knowledge, experience, and talents to deliver the results the business needs. These teams combine top-quality technical staff, such as data engineers, modellers, and statisticians, with those who understand the business side of the equation—people who can identify the questions that need to be answered, tell stories that illuminate data-led insights, and persuade and inspire others in the organization to act or change. In Deloitte’s Global IDO Survey, 62 percent of respondents say their struggle is to bring in talent with information design skills; another 66 percent find it challenging to find talent with expertise in user design.

It’s time for organizations to stop searching for superstars in an effort to build the “perfect” analytics team. It’s time they start building the best analytics team they can—a combination of talented individuals who work together to get work done and deliver meaningful results today, not at some point in the future. It’s time to build teams that blend technical wizardry, data engineering, and modelling smarts with business savvy and organizational fluency.

According to our 2018 insight-driven

organizations (IDO) survey, organizations that claim to be more analytically mature−

that embed data and analysis into their

decision making−are more likely to indicate

“people” as an important dimension to focus on for

analytics success.

While organizations realize the importance of talent, only 25 percent of executives surveyed for Deloitte’s 4.0 Industry Survey are highly confident that they have the right workforce compositions and skillsets needed in the future.

In Canada, 60 percent of organizations still lack a dedicated analytics team.

4 | Searching for superstars isn’t the answer |© Deloitte LLP and affiliated entities. 5

Here’s how to do it:

Stop chasing trends Use analytics’ role and focus to guide the search for talent

Most organizations understand that analytics is critical to the current and future success of their business. But they’re not always clear on what that role is, or where their analytics team should focus its efforts. This vagueness of purpose can result in analytics teams failing to provide the kind of insights needed to advance the business—because they don’t have the right talent. Instead of aligning recruitment to a specific, agreed-upon role for analytics, organizations can easily follow the latest hot talent trend, whether that’s a Hadoop expert, machine learning specialist, or describing nearly any role as a “data scientist,” to fill the positions. The result can be misaligned, lopsided teams with poor chemistry and sizable skill, knowledge, and experience gaps.

Organizations hunting for analytics talent need to focus on the here and now and set aside fanciful visions of their distant, analytics-powered future. They should instead concentrate on setting up themselves—and their analytics teams—for success over the next six to 12 months. Focusing on shorter timeframes will better enable the organization to invest time and resources in using analytics to tackle existing business challenges, achieving quick wins that can be used as the foundation and to create momentum for expanded analytics use in the future. Organizations that can see and feel the impact of analytics transform faster.

First, companies must identify the role analytics will play in the enterprise, whether supporting decision-making, producing a variety of reports, or enabling the enterprise in other ways. Is the analytics function primarily envisioned as a strategic advisor that provides actionable insights to aid in C-suite decision-making—for example, around customer targetting or the customer experience? Is analytics expected to enable the enterprise through education, awareness, and self-service? Or is the analytics team primarily responsible for producing regular reports on various topics? The answers will play an influential part in determining the kind of talent the company requires.

Organizations with more mature analytics functions also consider what the enterprise is focusing on or optimizing for in the short term (six to 12 months). Perhaps it’s agility or speed, because of plans for rapid expansion. Maybe it’s compliance, owing to new regulations coming into effect. It could be that market forces are pressuring organizations throughout the industry to become more digital or to embrace open platforms.

The role and focus of analytics can’t be determined in isolation. The C-suite, representatives of the business, and other internal analytics customers must work closely on it. This will keep all parties aligned around a common purpose and shared expectations.

Insight-driven organizations use this co-developed role and focus to guide their recruitment efforts and ensure they bring in the right talent for what the company wants to achieve. Recruiters and hiring managers, equipped with a better understanding of what the organization is trying to achieve, can zero in on the specific mix of skills, experience, and attitudes needed to find and hire candidates who can help get results now. If analytics’ role or focus aren’t well-defined, it’s all too easy for organizations to recruit people whose talents, though considerable, are fundamentally mismatched to the actual task at hand.

Data science is a process, not a job descriptionEnhance the process by taking a hard look at each step Insight-driven organizations aren’t created by individual talent as individual genius is difficult, if not impossible, to operationalize. Process is what transforms an ordinary organization into an insight-driven one—the process through which insights become action. That’s why reaching an agreement on the role and focus of the analytics function is an important first step in determining a company’s analytics talent needs. Understanding which parts of the company’s data science or insight process can, or needs to, improve will help the organization zero in on the precise mix of skills and experience that needs to be added to the analytics team.

Aspiring insight-driven organizations look for opportunities to improve all along their insight process (Figure 1). This work begins with a discovery of the key business problems that analytics can be used to solve. It then moves through data preparation, analysis planning and execution, communication of analysis results, and finally, operationalization of insights. Once gaps in the process are identified, stakeholders need to agree on which gaps are most critical to resolve now. The hiring team needs to understand these priorities, and align their recruiting effort to them.

Organizations searching for analytics talent need

to focus on what they need to achieve now, not

in a few years.

Organizations are realizing analytics success requires more than just data and technology. Our 2018 IDO survey found that, of the five key IDO pillars of strategy, people, process, data, and technology, the largest overall increases from 2017 to 2018 were:

Process +9%

People +6%


Figure 1

The insight processIn our experience working with clients, organizations encounter common gaps at each stage of their insight process (Figure 1). Among many, these gaps can include: poorly defined business problems; analytics models that can’t be implemented; inconsistent, poor-quality, or irrelevant data; overlooked, insight-skewing data biases; lack of contingency planning; weak statistical or data-mining skills; inability to communicate insights in a way the business understands or how the insights contribute to the business; and difficulties putting the insights into operation.

Closing these gaps typically requires particular combinations of skills and attitudes (see Figure 2). Different phases of the insight process often emphasize different capabilities. Some phases favour people with what we call “red” skills, such as quantitative analysts, data engineers, and computer scientists. Other phases have a greater need for those with “blue” skills (e.g., business acumen, visualization, or storytelling).

In some cases, “purple” leaders—those with both red and blue skillsets, such as an experienced analytics professional who also is adept at navigating the organization, explaining what insights mean, and persuading people to take action—can be the best fit. Of course, finding purple talent isn’t that easy.

Model and assess Move to deploy

Def ne target for analysis Identify features

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Identify the outcome Know your audience Structure your narra

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Release, transition and learn “Last mile”

Discover Data Design

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Data PreparationDiscovery

Operationalization

• Defining solutions that are not scalable/ transferable (low-effort impact)

• Data quality: lack of focus on consistency

• Focus on data that may not be operationalized due to compliance or IT; i.e., legacy systems

• Focus on data that has no or little potential to impact

• Poorly defined business hypotheses leading to re-work

• Lack of contingency planning for unforeseen risks

• Biases in data not taken into account, leading to wrong insights

• Poor judgment on when/where to stop the analysis

• Weak technical skills in data-mining and statistics

• Inability to multi-task

• Inability to translate numeric insights into plain language business can understand

• Inability to illus-trate how insights contribute to organizational objectives

• Difficulty in putting delivered solutions into actual usage within affected areas of the organization

• Inability to repeat the process of taking a project from question to deployment

Common gaps

Advanced data design & management journey

Advanced analytics & modelling journey

Information design & visualization journey

Storytelling journey

• Analytics model cannot be implemented

• Poorly defined business problems


Figure 2

The core analytics capabilities at each stage of the insight process

Macro-perspective and business acumenUnderstanding of the big picture, the company’s overall business strategy, services and products, current business issues, priorities and industry trends

Demand generation and strategic thinkingAbility to generate or gather the right demand for analytics services and be able to help business lines prioritize valuable and viable analytics projects

Data and technology literacyAbility to understand and assess how different internal, external and trending data and technology capabilities can be leveraged to solve business problems

Data discoveryAbility to translate a business issue into data requirements and assess existing/ new or potential data sources against availability, quality and accessibility criteria

Data engineeringAbility to extract and transform different types of data (numeric, text etc.) from disparate systems into a single location for analysis

Data quality assuranceAbility to assess the quality of data for its correctness and completeness using business sense as well as statistical methodologies

Data modellingAbility to combine and structure different levels of data into a unified analytics data set (ADS) that is built to the purpose of solving a specific business challenge

Data analysisAbility to use statistics, machine learning and data mining techniques to convert data into insights

Insight assessmentAbility to assess insights with stakeholders for validity and applicability to the business problem

Business commentary/storytelling Articulation of insights to explain outputs of data and analytics in the context of the audience’s needs

Visualization and designSkills in data visualization tools and training in graphic design principles to best present findings

Technology solution development Ability to develop technology solution (e.g. APIs, dashboard) to enable the consumption of insights

Insight activation Ability to support the application of insights through people and process enablers or incentives (policy design, process re-engineering, product design)

DiscoveryData preparation

Analytical modelling

In

sight communication Operationalization

Project Management Ability to clarify requirements, define scope with milestones and dependencies and agree on deliverables upfront. Throughout the project ,ability to execute on the plan within time and budget while meeting stakeholders expectations


Assessing how effectively an organization can deliver on the core capabilities along each stage of the insight process helps illuminate both strengths and weaknesses. Those weak-nesses can help refine the talent search and focus the organization on plugging key gaps. It’s important to note that multiple gaps don’t automatically mean multiple hires. Depending on an organization’s size, sector, and analytics maturity, or a candidate’s experience and seniority, it’s entirely possible one candidate could provide a number of the capabilities needed. That doesn’t mean organizations should hold out for the perfect candidate, a human “silver bullet” who can somehow solve all of the organization’s analytics problems. It’s far better to assemble a team of individuals who, combined, have the capabilities, experience, knowledge, humility, and discipline to execute the insight- process.

Recruiters need to be patient in their search to ensure the company brings in people who can truly help achieve the organization’s goals. At best, rushing to fill vacancies increases the chance of subpar performance and dysfunctional behaviour; at worst, it can lead to the organization’s disillusionment with the value of analytics.

When it comes to skills, recruiters also need to avoid emphasizing technical or language skills over other potentially softer skills, no matter the role being hired for or the process gap being filled. Intelligence, attention to detail, creative thinking, business-mindedness, and communication skills are key to the success of an analytics team, and these qualities should be sought in any candidate.

Business cases that involve actual data (e.g., setting up an equation, solving for an unknown), and require the candidate to explore the data, extract insights, and translate those into hypothetical actions are an excellent way to identify talent that can think analytically, communicate effectively, and demonstrate business acumen. Deloitte uses such cases in our own hiring, and it has proven invaluable for finding individuals with the right mix of attitude, aptitude, and business reasoning.

If there’s one deal-breaker for any candidate, it’s problem-solving. It’s critical that the analytics team shares a driving passion for uncovering and solving important business problems. A team that lacks passion for problem-solving—or a team recruited to an organization that lacks an insight process—can easily find itself conducting so-called science experiments that produce results of little use to the organization, wasting their time and talent and the company’s resources.

Think beyond traditional ways to acquire skills All the skills a company needs don’t have to reside in one discrete team

Building an effective analytics team is just that: building a team. Recruiting a single superstar talent isn’t going to deliver alone, nor will building a team comprised solely of PhDs, data engineers, statisticians, or computer scientists. An effective analytics team requires a range of talent prepared to get out of the back room. And some of that talent might not even be employed by you.

Co-create purple internal teams

To build an analytics team that gets results, organizations should take a two-pronged approach that demands a wider perspective and an open mind. Internally, they must collaborate to co-create purple teams that combine red technical talent with blue business skills. Data modellers, data architects, software developers, and information designers can be vital to any analytics team, to be sure, but companies also need savvy political representatives from the business lines who can connect analytics insights to strategic business questions.

For example, a retailer might want representatives from sales and marketing, merchandising, and finance; a financial institution could include members from risk, products, marketing, and customers; and a manufacturer or utility company could bring in people from operations, sales, and human resources. In this way, truly high-performing analytics teams bring not only a diversity of skills to their organization, but also a diversity of thought that can make all the difference.

Multiple gaps don’t have to mean multiple hires.

Yet organizations shouldn’t hold out for “silver bullet”

candidates to solve all their insight process challenges.


To build an analytics team that gets results, organizations need a wider perspective and an open mind to the different capabilities required to turn data into insights and actions.

Our 2018 IDO Survey found that analytically mature organizations display well-balanced teams with a mix of quantitative/technical (red) and business acumen and communication (blue) capabilities.

Insight-driven organizations did report a slightly larger proportion of ‘blue’ skills including macro-perspective and business acumen, project management, insight activation and insight assessment.

IDO also have stronger operationalization capabilities including technology solution development, such as the ability to develop APIs or dashboards that enable the consumption of insights and insight activation, and the ability to support the application of insights within the organization through people and process enablers or incentives like policy design, process reengineering, or product design.

There’s a notable difference in the capabilities strength between analytically mature (IDOs) and less mature organizations or non-IDOs (see Figure 4). IDOs rate their operationalization capabilities 23 percent higher than non-IDOs. Modelling capabilities, which are traditionally rated as most desirable by many organizations, is actually where the differences between IDOs and non IDOs is the smallest: 16 percent

Figure 4. Analytically mature organizations have stronger operationalization capabilities

As their analytics maturity increases, organizations often begin to look for talent with strong purple potential: a quantitative analyst with strong business instincts, for example. Ideally, these individuals would also be able to not only learn but teach, instilling a purple mindset throughout the analytics team. Unfortunately, such talent is scarce because it takes time to develop—at Deloitte, much of our own purple talent has two or more decades of work experience.

However, organizations can overcome this challenge by developing their own purple talent—creating career paths that, over time, enable their people to add the red or blue skills and knowledge they need to become more purple. Red talent can shadow frontline users of their analysis to better understand how their work is used and how insights are activated in the field day-to-day, for example; this can also help assess red talent’s (blue) business acumen. Blue talent should be provided with opportunities to increase their analytics IQ and better understand the kind of questions that can be solved through data, thus creating more demand for analytics

In our IDO survey, 80 percent of respondents report they’re using upskilling to build their analytics capabilities. Indeed, we see more organizations investing time and effort to create and deliver new learning and development programs to their red and blue talent in order to bridge the gap between them.

Capability levels:

5 = Leading4 = Advanced3 = Defined2 = Developing1 = Non existent

Source: 2018 IDO survey results

Technical analytics capabilities Delta between mature and less mature organizations

Data quality assurance 23%

Data analysis 22%

Technology solution development 20%

Data modelling 17%

Data engineering 15%

Visualization and design 14%

Data discovery 5%

Business analytics capabilities Delta between mature and less mature organizations

Macro-perspective and business acumen 30%

Demand generation and strategic thinking 26%

Insight activation 25%

Insight assessment 24%

Project management 20%

Business commentary/storytelling 8%

Data and technology literacy 7%

3.58 out of 5 3.65 out of 5

InputPrioritized Business Issues formulated as questions

17% 19% 16% 17% 23%

OutputUsing the insights generated to evolve end-to-end business processes

% difference in capabilities between mature and less mature organizations

Figure 3. IDOs build balanced teams with slightly stronger blue capabilities


Boost capabilities by collaborating with external organizations

Organizations recruiting analytics talent need to face a harsh truth: they’ll never be able to bring in-house all the talent they need to seize competitive advantage as an insight-driven organization. With so many companies pursuing a finite pool of talent, most will turn to partnerships to achieve the right shade of purple to accomplish their business goals.

Teaming with external parties can help organizations gain access to rare skills and knowledge, hard-to-find capabilities, and advanced technologies they simply can’t find, or afford, on their own. It can also significantly improve their speed and agility, enabling them to move ahead with insight-driven work far more quickly that would be possible on their own.

It’s important to note we’re not talking about simply outsourcing analytics work to third parties. Organizations need to develop strategic partnerships with external providers who can not only deliver missing capabilities as needed, but co-develop intellectual property, share their expertise, and foster the transfer of knowledge throughout the relationship. In other words, organizations should capitalize on their outside partners to increase their own purple quality.

External partnerships can take many forms. Core projects might stay with an organization’s in-house analytics team but smaller, non-core projects might be handled through hack-athons or crowdsourced solutions to enable the discovery of unconventional ideas (and new talent). Short-term, niche, or point-in-time needs could be tackled by an agency, while larger, more complex, or longer-term projects for which a company lacks in-house talent could be co-created with an external analytics partner.

In searching for potential external collaborators, organizations should focus on those that offer more capabilities—red and blue—than are needed right now, because as the partnership matures, those capabilities may prove useful. We’ve found that co-delivery models are more effective when partners have blue skills, such as the ability to communicate across functions to gain buy-in and build alignment. Partners willing and able to share their knowledge with internal team members are especially valuable.

Develop a common language to break down barriers Clarity helps make the analytics team more effective

At the core of any effective analytics team is clarity—a clarity of vision, of purpose, and of language. It’s vital that all members of the analytics team and the organization are on the same page day to day. They need to be able to talk about the work being done and the problems to be solved using a common language that prevents misunderstandings and misinterpretation and that keeps everyone aligned with the team’s greater strategic goals.

Achieving this can be challenging. It’s not uncommon to find members of an analytics team or insight process describing the same data, process step, or problem in very different terms. This can be attributed to a lack of real dialogue about an issue owing to various factors, such “red vs. blue” differences, team members working in isolation, or an individual pursuing his or her own idiosyncratic vision.

Organizations can help break down barriers and foster better communication and shared understanding in a number of ways. Rotation programs, symposiums, and learning sessions can help red and blue team members gain more insight into how their colleagues think about problems and the work being done. Mentoring relationships—whether red mentors blue or vice versa—can also help deepen relationships, strengthen communications, build mutual understanding, and improve the overall purple quality of the analytics team. Agile environments and methodologies can play an invaluable part as well (see next point).

To help foster the desired culture, organizations can also make an effort to deliberately recognize and reward purple individuals, teams, behaviour, and actions that challenge the status quo, demonstrate curiosity, show tenacity in solving thorny business problems, and more. Such recognition helps champion “purpleness” while building camaraderie among team members.

Teaming with outside parties can

help organizations gain access to rare skills and

knowledge, and improve their speed and agility.


ContactsAudrey AncionSenior ManagerStrategic Analytics and [email protected]

Ozgur CetinerSenior ManagerStrategic Analytics and [email protected]

Snehal PatelSenior ManagerConsulting [email protected]

Use agile environments and methodologies Nurture a culture that keeps your analyticstalent engaged

Hiring talent is just half the battle, of course. To engage diverse analytics talent over time, companies need to sustain the right kind of culture. Insight-driven organizations use agile thinking and environments to accelerate the pace of change, inspire action, and engage their analytics talent. Legacy organizations should consider embracing agility as part of cultivating their teams.

Analytics teams typically thrive when given the opportunity to discover in an iterative, exploratory fashion. Agile environments enable the free flow of information, flatten hierarchies, break down siloes, and support idea-generation and sharing. Work is recursive, not sequential, which minimizes information loss. The lack of hierarchy means anyone can share their insights with a colleague or executive, often sparking new ideas and business strategies. As well, agile environments require red and blue talent to mix each day, through presentations, discussions, daily touch points, teaming up on coding, and more. Creating an environment where team members can learn from each other and continually grow their skills and knowledge can be a powerful way to improve talent retention.

Agile, as the approach is known, provides a safe space to challenge conventional thinking, allowing team members the freedom to think big, think differently, and generate excitement about the work to be done. It also offers an environment where failure is acceptable and even seen as an opportunity to speed up learning and skills development. This “move fast and break stuff” environment can be tremendously motivating to an analytics team.

Agile is especially useful for improving speed to market. It can enable the analytics team to capture the proverbial low-hanging fruit, realizing the quick wins that can swiftly prove the utility and relevance of analytics and providing a series of regular successes that keep the analytics team engaged and motivated. This in turn can help generate greater executive buy-in and sponsorship toward building an insight-driven organization, securing more budget and resources for training, talent, new projects—and future successes.

Don’t wait for superstarsBuild analytics teams that get work done today

As analytics becomes increasingly essential to the success of any business, world-class analytics talent is becoming incredibly difficult to find—and equally expensive to recruit. Yet too many organizations continue to base their recruitment plans around highly talented superstars in data analysis, computer science, and other technical areas. The result? Frustrated talent and analytics investments that fail to deliver what the organization needs now.

Insight-driven organizations understand that effective analytics teams require a range of talents. They build purple teams that combine red technical skills and blue business acumen to solve meaningful challenges and deliver the insights that propel the business forward.

It’s time for organizations to stop searching for superstars in an effort to build the perfect analytics team. It’s time to start building analytics teams that get work done today, not at some point in the future. To do that, organizations need to be clear about the role and purpose of their analytics function and what they need analytics to achieve in the short term, and to understand where the gaps are in their current insight process to better understand the kind of talent they need. They need to look for red and blue talent to achieve the optimal shade of purple that will provide useful, business-focused insights. They need to expand their idea of an analytics team to include outside partners. And they need to ensure their analytics teams are engaged and speaking the same language.

Analytics is complex. Building an analytics team doesn’t need to be.

Analytics teams thrive in agile environments that allow for the free

flow of information, flatten hierarchies and

break down silos.

Gianluigi DuiellaSenior ManagerStrategic Analytics & Modeling [email protected]

Contributors


deloitte.ca

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